3.0

credits

Average Course Rating

This course covers some of the core statistical techniques used in research and analysis. It is targeted to graduate students with limited prior experience in statistics but a willingness to learn statistical concepts and an enthusiasm for quantitative data analysis. The course will cover several techniques for describing data, estimating attributes of populations, and hypothesis testing. Some time will be spent reviewing and understanding analysis implications, assumptions and challenges when using different levels of measurement. The course will also discuss ANOVA, as well as predictive modeling with a particular focus on the role of regression (continuous and dichotomous dependent variables) in data analysis. The core of the course is the application of statistical concepts covered -- it will not focus on the mathematical and statistical computations behind the various techniques. The best way to learn this material is by working through examples and assigned problems, as well as reviewing the literature using the different approaches. Consequently, students will complete problem sets using SPSS, write a data analysis proposal and submit an article critique. These assignments aim to connect the concepts discussed in class with the tools of data analysis in practice.